58 citations · 69 across the 5 of their papers we have counts for
5 papers
Triple Disentangled Representation Learning for Multimodal Affective Analysis
Ying Zhou, Xuefeng Liang, Han Chen +3
Multimodal learning has exhibited a significant advantage in affective analysis tasks owing to the comprehensive information of various modalities, particularly the complementary i…
Learning Subjective Time-Series Data via Utopia Label Distribution Approximation
Wenxin Xu, Hexin Jiang, Xuefeng Liang +3
Subjective time-series regression (STR) tasks have gained increasing attention recently. However, most existing methods overlook the label distribution bias in STR data, which resu…
Gaussian-smoothed Imbalance Data Improves Speech Emotion Recognition
Xuefeng Liang, Hexin Jiang, Wenxin Xu +1
In speech emotion recognition tasks, models learn emotional representations from datasets. We find the data distribution in the IEMOCAP dataset is very imbalanced, which may harm m…
Tripartite: Tackle Noisy Labels by a More Precise Partition
Xuefeng Liang, Longshan Yao, Xingyu Liu +1
Samples in large-scale datasets may be mislabeled due to various reasons, and Deep Neural Networks can easily over-fit to the noisy label data. To tackle this problem, the key poin…
Multi-Classifier Interactive Learning for Ambiguous Speech Emotion Recognition
Ying Zhou, Xuefeng Liang, Yu Gu +2
In recent years, speech emotion recognition technology is of great significance in industrial applications such as call centers, social robots and health care. The combination of s…